Transforming ​PD, EAD and LGD Models to Meet IFRS and IRB Standards

Introduction: Why PD, EAD and LGD Matter

For one of our clients in the financial sector, capital adequacy models for Probability of Default (PD), Exposure at Default (EAD) and  Loss Given Default (LGD) required a comprehensive rebuild to meet updated IFRS and IRB regulatory standards. Their existing framework did not reflect the latest supervisory expectations, which posed both compliance challenges and operational risks.

“As regulators continued to tighten expectations, it became clear that our models needed to evolve,” recalls a Senior Risk Management Officer involved in the project. “We wanted not only to meet the requirements but to create a modeling framework that stands the test of time.”

Our Approach: Advanced Risk Modeling with Modern Methodologies

We began with a deep diagnostic review of the institution’s existing ​PD, EAD and LGD models, data sources, and assumptions. This initial assessment revealed several improvement opportunities in data structure, scoring logic, and macroeconomic sensitivity.

Our experts then implemented a suite of advanced statistical and machine learning methods, calibrated to the high demands of regulatory risk modeling:

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  Logistic regression with Weight of Evidence (WOE) transformations into stronger variable interpretability.
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  Elastic net regression for robust variable selection and regularization.
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  Decision trees and linear regression (including Tobit models) for ​binear and / or continuous outcomes.
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  Integration of macroeconomic components and methodological conservatism margins, ensuring resilience under stress-testing and regulatory scrutiny.

Implementation: From Data Preparation to Functional Specification

The turning point in the project emerged when we redesigned the modeling pipeline and data architecture to achieve full regulatory alignment.

First, we performed a detailed segmentation of exposures—by product type, client segment, and economic profile—to ensure behavioral and structural consistency within each model. For every segment, we defined precise data quality thresholds, designed scoring methodologies, and constructed macroeconomic overlays tailored to the specific business environment.

Advanced analytical techniques—including logistic regression with WOE transformations, elastic net calibration, and decision tree validation—were applied iteratively to enhance both model ​performance and interpretability.

Impact: Model Adequacy and Regulatory Confidence

The results of this transformation were clear.

The newly developed PD, EAD and LGD capital models fully met IFRS/IRB requirements, passing internal audit and external validation with strong results.

Key outcomes:

• Measurably improved model adequacy and predictive strength compared to legacy versions.
• Enhanced transparency and explainability, simplifying both internal approval and external regulator interactions.
• Smoother integration with IFRS reporting and risk monitoring systems.
• Greater efficiency and alignment across modeling, validation, and risk functions.

Enablement & Training: Empowering the Client’s Team

To ensure lasting success, we conducted a comprehensive enablement program for the client’s risk and analytics teams.

Through tailored workshops and on‑the‑job training sessions, the client’s staff gained hands‑on experience in interpreting, maintaining, and enhancing the new ​PD, EAD and LGD  models. This empowered their teams to independently navigate future regulatory updates and validation cycles.

Conclusion: